State of the art vehicle automation presently enables some vehicles, such as cars, to operate in a substantially and/or sometimes fully autonomous state. An ability of such autonomous agents to operate effectively and safely in busy or active environments often relies on an ability of the autonomous agent to observe its operating environment and make operating decisions that enables the autonomous agent to achieve a routing or traveling goal in a safe manner.
A technical problem that may arise in many operating circumstances involving an autonomous agent may relate to an inability of the autonomous agent to select or make the most optimal operating decision when multiple operating decisions are possible in a given operating circumstance. While route planning and low-level control instructions may provide a basis for performing self-controls by an autonomous agent for achieving a particular destination, behavioral planning typically provides a basis for performing real-time decisions by the autonomous agent according to live observations of the operating environment made by one or more sensors onboard the autonomous agent. In particular, the autonomous agent's perspective of its real-time environment for selecting behavioral policy is primarily shaped by onboard sensors carried by the autonomous agent. Thus, the technical problem persists because the autonomous agent may have only a singular view of its operating environment rather than comprehensive perspectives of the operating environment to enable an optimal selection of behavioral policy in real-time operating circumstances.
Thus, there is a need in the vehicle automation field for enabling a multi-perspective view of an operating environment of an autonomous agent that enables an optimal selection of behavioral policy by an autonomous agent in real-time operating conditions. The embodiments of the present application described herein provide technical solutions that address, at least, the need described above.